Predicting future injury in runners using participant-specific models of internal structural...

Predicting future injury in runners using participant-specific models of internal

structural loading

Background

• Incidence of running injury is higho 18.2 – 92.4%

• No decline despite years of research

Background

• Limitations1. External variables

vGRF Pronation Footwear Strike

Background

Background

ΔITBσ/t

PFCF or ΔPFCF/t

AJCF or ΔAJCF/t

PLσ or ΔPLσ/t

?

AJCF or ΔAJCF/t?

Background

• Limitations2. Cross-sectional and retrospective designs

Background

• Overcoming previous limitationso Estimates internal loadingo Prospective design

Presentation aims

• Discuss methods and analyses

RecruitmentStrength Testing

MoCap

Surveys

Nexus Visual3D OpenSim

Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

Recruitment

• n = 102o >18 yrs. oldo run ≥3 per wk.o no running-related/lower-limb injury within 6 mos.o no other sports at competitive level, or >2 per wk.


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

MoCap

• 45-marker set

• 50m indoor track

• 8 Kistler force plateso Embedded in series

• 12 Vicon cameras (T-40, T-160)

• 4 Hyun-Joon laser speed gates

MoCap

• 2 conditionso Constrained

3m/s±5% 5 min. ~18 to 20 laps (~3 to 3.3m/s)

o Typical “Use the pace that you run for the majority of your

mileage” 5 min. ~17 to 26 laps (~2.8 to 4.3m/s)


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

Vicon Nexus

• Next steps…o Label datao ~40 trials / participant x 102 participants

~4000 trials

Vicon Nexus


~4000 trials

S’up undergrads?

Vicon Nexus


~4000 trialsAuto-labelling?


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

Visual3D

• Next steps…o Export labelled datao Correct coordinate systemso Build an Inverse Kinematics modelo Calculate external variables

o joint forces and momentso vGRF, ΔvGRF/to pronationo etc…


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

Strength testing

• Biodexo Isotonic

Ankle, knee, hip 2 trials: 10°/s “away,” 60°/s “toward” 2 trials: 60°/s “away,” 10°/s “toward”


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

MatLab: Strength

• Next steps…o Participant-specific strength parameterso Write into OpenSim


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

OpenSim

• Next steps…o Build model…

Arnold et al., 2010 Lenhart et al., 2014 44 muscle lower-limb with patella

o Import… V3D IK models Strength parameters


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

Internal variables

Surveys• Baseline

o Sexo Experience

years competitive level

o Past habits other PA mileage location warm up

o Footwear shoes orthotics

o Lower-limb and running injury history

Surveys

• 26 weeklyo Other PAo Mileage, time per dayo Changes in footwearo Pain/Injury

Location Pain severity Differential diagnosis Changes in running/PA


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

Internal variables

~500 columns per participant per survey

102 participants x 26 surveys ~ 2600 surveys


MoCap

Surveys


Arnold 2010

Lenhart 2014

MatLab

MatLabor Excel

Analysis

External variables

Internal variables

Baseline and Prospective variables

Analysis

Analysis

Injury•# reported•Days missed•Pain

Proposed internal variable

Analysis

• Covariates...?

• More complex or hierarchical models…?

• Fitting data to theoretical curve…?

Analysis

Proposed internal variable

Estimated strides/week

Analysis

• Aggregate loading metric…?o Average z-score of each injury-related variable

Analysis

• Group-wise comparisons…?o Footstrike (fore vs. rear)o Footwear (minimal vs. support)

Predicting future injury in runners using participant-specific models of internal structural...

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